In the last few years, the technology of mobile computing has gained tremendous popularity and changed users mind for
computing. However, smart phones are constrained computing devices which are facing number of issues related to resources such as
memory, storage, computation power and shortened energy. To overcome these constraints, offloading provides natural solution for
mobile cloud environment by migrating the intensive problems to cloud servers. However, conventional frameworks of offloading lack in
considering the dynamic execution time as well as they have not focused on extra overhead of runtime migration. This paper proposed a
new approach for runtime offloading to achieve better performance and energy optimization.
Published In:IJCSN Journal Volume 8, Issue 3
Date of Publication : June 2019
Pages : 305-310
Tables : --
Nancy Arya :
Department of Computer Science, Banasthali Vidyapith
Sunita Choudhary :
Department of Computer Science, Banasthali Vidyapith
S. Taruna :
Department of Computer Science, JK Lakshmipat University
Mobile Computing, Offloading, Runtime Offloading, Energy Optimization
Traditional models have worked to speed up the execution
and save the energy for local mobile devices. It is very
important for the resource aspects. However, every model
has its own benefits and limitations. In the paper, the
author has proposed a novel technique for offloading the
computational tasks by improving the performance and
efficiency of energy. This work used the concept of
benchmarking of the computational task before actual
execution of the task and on the basis of the estimated
value for time and energy, the final decision has taken for
offloading. The decision depends on less energy
consumption. The results of experiment clearly shows that
the proposed work gives better results for energy
efficiency and performance. The work has carried out by
the execution of complex application of high complexity
such as matrix multiplication. For the large matrix size,
achieved efficiency around 93.1% for execution time and
96.2% for energy which proves the higher efficiency. The
results of the proposed work are then compared to the
results of existing framework and shows better efficiency
in energy optimization.
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